A water potential based on multipole moments trained by machine learning — Reducing maximum energy errors

Geometrically faithful homeomorphisms between the electron density and the bare nuclear potential

(2009) Paul L. A. Popelier et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning

(2009) Chris M. Handley et al.   Journal of Chemical Theory and Computation

Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning

(2009) Chris M. Handley et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Properties and 3D Structure of Liquid Water:  A Perspective from a High-Rank Multipolar Electrostatic Potential

(2008) Steven Y. Liem et al.   Journal of Chemical Theory and Computation

Beyond Point Charges: Dynamic Polarization from Neural Net Predicted Multipole Moments

(2008) Michael G. Darley et al.   Journal of Chemical Theory and Computation

Toward anab initiofragment database for bioisosterism: Dependence of QCT properties on level of theory, conformation, and chemical environment

(2008) Michael Devereux et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Subset selection from large datasets for Kriging modeling

(2008) Gijs Rennen   STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION